Data Mining Techniques 数据开采技术：营销、销售与客户关系管理

Packed with more than forty percent new and updated material, this edition shows business managers, marketing analysts, and data mining specialists how to harness fundamental data mining methods and techniques to solve common types of business problems.
Each chapter covers a new data mining technique, and then shows readers how to apply the technique for improved marketing, sales, and customer support.
The authors build on their reputation for concise, clear, and practical explanations of complex concepts, making this book the perfect introduction to data mining.
More advanced chapters cover such topics as how to prepare data for analysis and how to create the necessary infrastructure for data mining.
Covers core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, clustering, and survival analysis.

作者简介：
MICHAEL J. A. BERRY and GORDON S. LINOFF are the founders of Data Miners, Inc., a consultancy specializing in data mining. They have jointly authored some of the leading data mining titles in the field, Data Mining Techniques, Mastering Data Mining, and Mining the Web (all from Wiley). They each have more than a decade of experience applying data mining techniques to business problems in marketing and customer relationship management.

Acknowledgments
About the Authors
Introduction
Chapter 1: Why and What Is Data Mining?
Chapter 2: The Virtuous Cycle of Data Mining
Chapter 3: Data Mining Methodology and Best Practices
Chapter 4: Data Mining Applications in Marketing and Customer Relationship Management
Chapter 5: The Lure of Statistics: Data Mining Using Familiar Tools
Chapter 6: Decision Trees
Chapter 7: Artificial Neural Networks
Chapter 8: Nearest Neighbor Approaches: Memory-Based Reasoning and Collaborative Filtering
Chapter 9: Market Basket Analysis and Association Rules
Chapter 10: Link Analysis
Chapter 11: Automatic Cluster Detection
Chapter 12: Knowing When to Worry: Hazard Functions and Survival Analysis in Marketing
Chapter 13: Genetic Algorithms
Chapter 14: Data Mining throughout the Customer Life Cycle
Chapter 15: Data Warehousing, OLAP, and Data Mining
Chapter 16: Building the Data Mining Environment
Chapter 17: Preparing Data for Mining
Chapter 18: Putting Data Mining to Work
Index